The effects of a 6-month resistance training and dried plum consumption intervention on strength, body composition, blood markers of bone turnover, and inflammation in breast cancer survivors
Bibliographic record
Abstract
The purpose of this study was to examine the effects of resistance training (RT) and dried plum (DP) consumption on strength, body composition, blood markers of bone, and inflammation in breast cancer survivors (BCS). Twenty-three BCS (RT, n = 12; RT+DP, n = 11), aged 64 ± 7 years, were evaluated at baseline and after 6 months of intervention on the following: muscular strength (chest press and leg extension) via 1-repetition maximums (1RMs); body composition, specifically bone mineral density (BMD) by dual energy X-ray absorptiometry; biochemical markers of bone turnover (bone-specific alkaline phosphatase (BAP), tartrate resistant acid phosphatase (TRAP-5b)); and inflammation (C-reactive protein (CRP)). Target RT prescription was 2 days/week of 10 exercises, including 2 sets of 8-12 repetitions at ∼60%-80% of 1RM. RT+DP also consumed 90 g of DP daily. There were no baseline differences between groups or any group-by-time interactions for any of the variables. BCS increased upper (p < 0.05) (RT: 64 ± 14 to 80 ± 17 kg; RT+DP: 72 ± 23 to 91 ± 20 kg) and lower (p < 0.05) (RT: 69 ± 20 to 87 ± 28 kg; RT+DP: 78 ± 19 to 100 ± 21 kg) body strength. Body composition and BMD improvements were not observed. TRAP-5b decreased in the RT group (p < 0.05) (4.55 ± 1.57 to 4.04 ± 1.63 U/L) and the RT+DP group (p = 0.07) (5.10 ± 2.75 to 4.27 ± 2.03 U/L). Changes in BAP and CRP were not observed. RT was effective for improving biochemical markers of bone turnover and muscular strength in BCS. A longer and higher intensity intervention may be needed to reveal the true effects of RT and DP on body composition and biochemical markers of inflammation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".